import pandas as pd
from pandas_profiling import ProfileReport
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sb
df = pd.read_csv("./Data/raw_house_data.csv")
df.head()
| MLS | sold_price | zipcode | longitude | latitude | lot_acres | taxes | year_built | bedrooms | bathrooms | sqrt_ft | garage | kitchen_features | fireplaces | floor_covering | HOA | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 21530491 | 5300000.0 | 85637 | -110.378200 | 31.356362 | 2154.00 | 5272.00 | 1941 | 13 | 10 | 10500 | 0 | Dishwasher, Freezer, Refrigerator, Oven | 6 | Mexican Tile, Wood | 0 |
| 1 | 21529082 | 4200000.0 | 85646 | -111.045371 | 31.594213 | 1707.00 | 10422.36 | 1997 | 2 | 2 | 7300 | 0 | Dishwasher, Garbage Disposal | 5 | Natural Stone, Other | 0 |
| 2 | 3054672 | 4200000.0 | 85646 | -111.040707 | 31.594844 | 1707.00 | 10482.00 | 1997 | 2 | 3 | None | None | Dishwasher, Garbage Disposal, Refrigerator | 5 | Natural Stone, Other: Rock | None |
| 3 | 21919321 | 4500000.0 | 85646 | -111.035925 | 31.645878 | 636.67 | 8418.58 | 1930 | 7 | 5 | 9019 | 4 | Dishwasher, Double Sink, Pantry: Butler, Refri... | 4 | Ceramic Tile, Laminate, Wood | None |
| 4 | 21306357 | 3411450.0 | 85750 | -110.813768 | 32.285162 | 3.21 | 15393.00 | 1995 | 4 | 6 | 6396 | 3 | Dishwasher, Garbage Disposal, Refrigerator, Mi... | 5 | Carpet, Concrete | 55 |
df.shape
(5000, 16)
df.describe()
| MLS | sold_price | zipcode | longitude | latitude | lot_acres | taxes | year_built | bedrooms | |
|---|---|---|---|---|---|---|---|---|---|
| count | 5.000000e+03 | 5.000000e+03 | 5000.000000 | 5000.000000 | 5000.000000 | 4990.000000 | 5.000000e+03 | 5000.00000 | 5000.000000 |
| mean | 2.127070e+07 | 7.746262e+05 | 85723.025600 | -110.912107 | 32.308512 | 4.661317 | 9.402828e+03 | 1992.32800 | 3.933800 |
| std | 2.398508e+06 | 3.185556e+05 | 38.061712 | 0.120629 | 0.178028 | 51.685230 | 1.729385e+05 | 65.48614 | 1.245362 |
| min | 3.042851e+06 | 1.690000e+05 | 85118.000000 | -112.520168 | 31.356362 | 0.000000 | 0.000000e+00 | 0.00000 | 1.000000 |
| 25% | 2.140718e+07 | 5.850000e+05 | 85718.000000 | -110.979260 | 32.277484 | 0.580000 | 4.803605e+03 | 1987.00000 | 3.000000 |
| 50% | 2.161469e+07 | 6.750000e+05 | 85737.000000 | -110.923420 | 32.318517 | 0.990000 | 6.223760e+03 | 1999.00000 | 4.000000 |
| 75% | 2.180480e+07 | 8.350000e+05 | 85749.000000 | -110.859078 | 32.394334 | 1.757500 | 8.082830e+03 | 2006.00000 | 4.000000 |
| max | 2.192856e+07 | 5.300000e+06 | 86323.000000 | -109.454637 | 34.927884 | 2154.000000 | 1.221508e+07 | 2019.00000 | 36.000000 |
df.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 5000 entries, 0 to 4999 Data columns (total 16 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 MLS 5000 non-null int64 1 sold_price 5000 non-null float64 2 zipcode 5000 non-null int64 3 longitude 5000 non-null float64 4 latitude 5000 non-null float64 5 lot_acres 4990 non-null float64 6 taxes 5000 non-null float64 7 year_built 5000 non-null int64 8 bedrooms 5000 non-null int64 9 bathrooms 5000 non-null object 10 sqrt_ft 5000 non-null object 11 garage 5000 non-null object 12 kitchen_features 5000 non-null object 13 fireplaces 5000 non-null object 14 floor_covering 5000 non-null object 15 HOA 5000 non-null object dtypes: float64(5), int64(4), object(7) memory usage: 625.1+ KB
df["kitchen_features"] = df["kitchen_features"].astype("category")
df["floor_covering"] = df["floor_covering"].astype("category")
kitchen_features and floor_covering could be encoded with TF-IDF, word2vec, or fasttext
len(df[df["lot_acres"]==0]) / len(df)
0.007
len(df[df["taxes"]==0]) / len(df)
0.0044
len(df[df["year_built"]==0]) / len(df)
0.001
for i in df.columns:
print(i)
print(df[df[i]=="None"].count()/len(df))
MLS MLS 0.0 sold_price 0.0 zipcode 0.0 longitude 0.0 latitude 0.0 lot_acres 0.0 taxes 0.0 year_built 0.0 bedrooms 0.0 bathrooms 0.0 sqrt_ft 0.0 garage 0.0 kitchen_features 0.0 fireplaces 0.0 floor_covering 0.0 HOA 0.0 dtype: float64 sold_price MLS 0.0 sold_price 0.0 zipcode 0.0 longitude 0.0 latitude 0.0 lot_acres 0.0 taxes 0.0 year_built 0.0 bedrooms 0.0 bathrooms 0.0 sqrt_ft 0.0 garage 0.0 kitchen_features 0.0 fireplaces 0.0 floor_covering 0.0 HOA 0.0 dtype: float64 zipcode MLS 0.0 sold_price 0.0 zipcode 0.0 longitude 0.0 latitude 0.0 lot_acres 0.0 taxes 0.0 year_built 0.0 bedrooms 0.0 bathrooms 0.0 sqrt_ft 0.0 garage 0.0 kitchen_features 0.0 fireplaces 0.0 floor_covering 0.0 HOA 0.0 dtype: float64 longitude MLS 0.0 sold_price 0.0 zipcode 0.0 longitude 0.0 latitude 0.0 lot_acres 0.0 taxes 0.0 year_built 0.0 bedrooms 0.0 bathrooms 0.0 sqrt_ft 0.0 garage 0.0 kitchen_features 0.0 fireplaces 0.0 floor_covering 0.0 HOA 0.0 dtype: float64 latitude MLS 0.0 sold_price 0.0 zipcode 0.0 longitude 0.0 latitude 0.0 lot_acres 0.0 taxes 0.0 year_built 0.0 bedrooms 0.0 bathrooms 0.0 sqrt_ft 0.0 garage 0.0 kitchen_features 0.0 fireplaces 0.0 floor_covering 0.0 HOA 0.0 dtype: float64 lot_acres MLS 0.0 sold_price 0.0 zipcode 0.0 longitude 0.0 latitude 0.0 lot_acres 0.0 taxes 0.0 year_built 0.0 bedrooms 0.0 bathrooms 0.0 sqrt_ft 0.0 garage 0.0 kitchen_features 0.0 fireplaces 0.0 floor_covering 0.0 HOA 0.0 dtype: float64 taxes MLS 0.0 sold_price 0.0 zipcode 0.0 longitude 0.0 latitude 0.0 lot_acres 0.0 taxes 0.0 year_built 0.0 bedrooms 0.0 bathrooms 0.0 sqrt_ft 0.0 garage 0.0 kitchen_features 0.0 fireplaces 0.0 floor_covering 0.0 HOA 0.0 dtype: float64 year_built MLS 0.0 sold_price 0.0 zipcode 0.0 longitude 0.0 latitude 0.0 lot_acres 0.0 taxes 0.0 year_built 0.0 bedrooms 0.0 bathrooms 0.0 sqrt_ft 0.0 garage 0.0 kitchen_features 0.0 fireplaces 0.0 floor_covering 0.0 HOA 0.0 dtype: float64 bedrooms MLS 0.0 sold_price 0.0 zipcode 0.0 longitude 0.0 latitude 0.0 lot_acres 0.0 taxes 0.0 year_built 0.0 bedrooms 0.0 bathrooms 0.0 sqrt_ft 0.0 garage 0.0 kitchen_features 0.0 fireplaces 0.0 floor_covering 0.0 HOA 0.0 dtype: float64 bathrooms MLS 0.0012 sold_price 0.0012 zipcode 0.0012 longitude 0.0012 latitude 0.0012 lot_acres 0.0012 taxes 0.0012 year_built 0.0012 bedrooms 0.0012 bathrooms 0.0012 sqrt_ft 0.0012 garage 0.0012 kitchen_features 0.0012 fireplaces 0.0012 floor_covering 0.0012 HOA 0.0012 dtype: float64 sqrt_ft MLS 0.0112 sold_price 0.0112 zipcode 0.0112 longitude 0.0112 latitude 0.0112 lot_acres 0.0092 taxes 0.0112 year_built 0.0112 bedrooms 0.0112 bathrooms 0.0112 sqrt_ft 0.0112 garage 0.0112 kitchen_features 0.0112 fireplaces 0.0112 floor_covering 0.0112 HOA 0.0112 dtype: float64 garage MLS 0.0014 sold_price 0.0014 zipcode 0.0014 longitude 0.0014 latitude 0.0014 lot_acres 0.0014 taxes 0.0014 year_built 0.0014 bedrooms 0.0014 bathrooms 0.0014 sqrt_ft 0.0014 garage 0.0014 kitchen_features 0.0014 fireplaces 0.0014 floor_covering 0.0014 HOA 0.0014 dtype: float64 kitchen_features MLS 0.0066 sold_price 0.0066 zipcode 0.0066 longitude 0.0066 latitude 0.0066 lot_acres 0.0066 taxes 0.0066 year_built 0.0066 bedrooms 0.0066 bathrooms 0.0066 sqrt_ft 0.0066 garage 0.0066 kitchen_features 0.0066 fireplaces 0.0066 floor_covering 0.0066 HOA 0.0066 dtype: float64 fireplaces MLS 0.0 sold_price 0.0 zipcode 0.0 longitude 0.0 latitude 0.0 lot_acres 0.0 taxes 0.0 year_built 0.0 bedrooms 0.0 bathrooms 0.0 sqrt_ft 0.0 garage 0.0 kitchen_features 0.0 fireplaces 0.0 floor_covering 0.0 HOA 0.0 dtype: float64 floor_covering MLS 0.0002 sold_price 0.0002 zipcode 0.0002 longitude 0.0002 latitude 0.0002 lot_acres 0.0002 taxes 0.0002 year_built 0.0002 bedrooms 0.0002 bathrooms 0.0002 sqrt_ft 0.0002 garage 0.0002 kitchen_features 0.0002 fireplaces 0.0002 floor_covering 0.0002 HOA 0.0002 dtype: float64 HOA MLS 0.1124 sold_price 0.1124 zipcode 0.1124 longitude 0.1124 latitude 0.1124 lot_acres 0.1118 taxes 0.1124 year_built 0.1124 bedrooms 0.1124 bathrooms 0.1124 sqrt_ft 0.1124 garage 0.1124 kitchen_features 0.1124 fireplaces 0.1124 floor_covering 0.1124 HOA 0.1124 dtype: float64
for i in df.columns:
print(i)
print(df[df[i] == 0].count()/len(df))
#lot_acres, taxes, year_built
MLS MLS 0.0 sold_price 0.0 zipcode 0.0 longitude 0.0 latitude 0.0 lot_acres 0.0 taxes 0.0 year_built 0.0 bedrooms 0.0 bathrooms 0.0 sqrt_ft 0.0 garage 0.0 kitchen_features 0.0 fireplaces 0.0 floor_covering 0.0 HOA 0.0 dtype: float64 sold_price MLS 0.0 sold_price 0.0 zipcode 0.0 longitude 0.0 latitude 0.0 lot_acres 0.0 taxes 0.0 year_built 0.0 bedrooms 0.0 bathrooms 0.0 sqrt_ft 0.0 garage 0.0 kitchen_features 0.0 fireplaces 0.0 floor_covering 0.0 HOA 0.0 dtype: float64 zipcode MLS 0.0 sold_price 0.0 zipcode 0.0 longitude 0.0 latitude 0.0 lot_acres 0.0 taxes 0.0 year_built 0.0 bedrooms 0.0 bathrooms 0.0 sqrt_ft 0.0 garage 0.0 kitchen_features 0.0 fireplaces 0.0 floor_covering 0.0 HOA 0.0 dtype: float64 longitude MLS 0.0 sold_price 0.0 zipcode 0.0 longitude 0.0 latitude 0.0 lot_acres 0.0 taxes 0.0 year_built 0.0 bedrooms 0.0 bathrooms 0.0 sqrt_ft 0.0 garage 0.0 kitchen_features 0.0 fireplaces 0.0 floor_covering 0.0 HOA 0.0 dtype: float64 latitude MLS 0.0 sold_price 0.0 zipcode 0.0 longitude 0.0 latitude 0.0 lot_acres 0.0 taxes 0.0 year_built 0.0 bedrooms 0.0 bathrooms 0.0 sqrt_ft 0.0 garage 0.0 kitchen_features 0.0 fireplaces 0.0 floor_covering 0.0 HOA 0.0 dtype: float64 lot_acres MLS 0.007 sold_price 0.007 zipcode 0.007 longitude 0.007 latitude 0.007 lot_acres 0.007 taxes 0.007 year_built 0.007 bedrooms 0.007 bathrooms 0.007 sqrt_ft 0.007 garage 0.007 kitchen_features 0.007 fireplaces 0.007 floor_covering 0.007 HOA 0.007 dtype: float64 taxes MLS 0.0044 sold_price 0.0044 zipcode 0.0044 longitude 0.0044 latitude 0.0044 lot_acres 0.0044 taxes 0.0044 year_built 0.0044 bedrooms 0.0044 bathrooms 0.0044 sqrt_ft 0.0044 garage 0.0044 kitchen_features 0.0044 fireplaces 0.0044 floor_covering 0.0044 HOA 0.0044 dtype: float64 year_built MLS 0.001 sold_price 0.001 zipcode 0.001 longitude 0.001 latitude 0.001 lot_acres 0.001 taxes 0.001 year_built 0.001 bedrooms 0.001 bathrooms 0.001 sqrt_ft 0.001 garage 0.001 kitchen_features 0.001 fireplaces 0.001 floor_covering 0.001 HOA 0.001 dtype: float64 bedrooms MLS 0.0 sold_price 0.0 zipcode 0.0 longitude 0.0 latitude 0.0 lot_acres 0.0 taxes 0.0 year_built 0.0 bedrooms 0.0 bathrooms 0.0 sqrt_ft 0.0 garage 0.0 kitchen_features 0.0 fireplaces 0.0 floor_covering 0.0 HOA 0.0 dtype: float64 bathrooms MLS 0.0 sold_price 0.0 zipcode 0.0 longitude 0.0 latitude 0.0 lot_acres 0.0 taxes 0.0 year_built 0.0 bedrooms 0.0 bathrooms 0.0 sqrt_ft 0.0 garage 0.0 kitchen_features 0.0 fireplaces 0.0 floor_covering 0.0 HOA 0.0 dtype: float64 sqrt_ft MLS 0.0 sold_price 0.0 zipcode 0.0 longitude 0.0 latitude 0.0 lot_acres 0.0 taxes 0.0 year_built 0.0 bedrooms 0.0 bathrooms 0.0 sqrt_ft 0.0 garage 0.0 kitchen_features 0.0 fireplaces 0.0 floor_covering 0.0 HOA 0.0 dtype: float64 garage MLS 0.0 sold_price 0.0 zipcode 0.0 longitude 0.0 latitude 0.0 lot_acres 0.0 taxes 0.0 year_built 0.0 bedrooms 0.0 bathrooms 0.0 sqrt_ft 0.0 garage 0.0 kitchen_features 0.0 fireplaces 0.0 floor_covering 0.0 HOA 0.0 dtype: float64 kitchen_features MLS 0.0 sold_price 0.0 zipcode 0.0 longitude 0.0 latitude 0.0 lot_acres 0.0 taxes 0.0 year_built 0.0 bedrooms 0.0 bathrooms 0.0 sqrt_ft 0.0 garage 0.0 kitchen_features 0.0 fireplaces 0.0 floor_covering 0.0 HOA 0.0 dtype: float64 fireplaces MLS 0.0 sold_price 0.0 zipcode 0.0 longitude 0.0 latitude 0.0 lot_acres 0.0 taxes 0.0 year_built 0.0 bedrooms 0.0 bathrooms 0.0 sqrt_ft 0.0 garage 0.0 kitchen_features 0.0 fireplaces 0.0 floor_covering 0.0 HOA 0.0 dtype: float64 floor_covering MLS 0.0 sold_price 0.0 zipcode 0.0 longitude 0.0 latitude 0.0 lot_acres 0.0 taxes 0.0 year_built 0.0 bedrooms 0.0 bathrooms 0.0 sqrt_ft 0.0 garage 0.0 kitchen_features 0.0 fireplaces 0.0 floor_covering 0.0 HOA 0.0 dtype: float64 HOA MLS 0.0 sold_price 0.0 zipcode 0.0 longitude 0.0 latitude 0.0 lot_acres 0.0 taxes 0.0 year_built 0.0 bedrooms 0.0 bathrooms 0.0 sqrt_ft 0.0 garage 0.0 kitchen_features 0.0 fireplaces 0.0 floor_covering 0.0 HOA 0.0 dtype: float64
df['HOA'].replace('None', 0, inplace=True)
df['bathrooms'].replace('None', 0, inplace=True)
df['garage'].replace('None', 0, inplace=True)
df["sqrt_ft"].replace('None', 0, inplace=True)
df["sqrt_ft"] = df["sqrt_ft"].astype('float')
df["sqrt_ft"] = df.groupby(['bedrooms']).transform(lambda x: x.replace(0, x.mean()))['sqrt_ft']
df["year_built"].value_counts()
2006 247
2007 236
2002 235
2005 230
2004 202
...
1907 1
1921 1
1910 1
1914 1
1927 1
Name: year_built, Length: 112, dtype: int64
df["year_built"].replace(0, 2006, inplace=True)
df["taxes"] = df.groupby(['bedrooms']).transform(lambda x: x.replace(0, x.mean()))['taxes']
df["lot_acres"] = df.groupby(['bedrooms']).transform(lambda x: x.replace(0, x.mean()))['lot_acres']
df["bathrooms"] = df["bathrooms"].astype('float')
df["garage"] = df["garage"].astype('float')
df["HOA"] = df['HOA'].str.replace(',', '', regex=False)
df["HOA"] = df["HOA"].astype('float')
df["kitchen_features"].value_counts()
Dishwasher, Garbage Disposal, Refrigerator, Microwave, Oven 1719
Dishwasher, Garbage Disposal, Microwave, Oven 270
Compactor, Dishwasher, Garbage Disposal, Refrigerator, Microwave, Oven 189
Dishwasher, Garbage Disposal, Refrigerator, Oven 181
Dishwasher, Freezer, Garbage Disposal, Refrigerator, Microwave, Oven 127
...
Dishwasher, Double Sink, Garbage Disposal, Pantry: Walk-In, Refrigerator, Appliance Color: Stainless, Countertops: Granite 1
Dishwasher, Double Sink, Garbage Disposal, Pantry: Walk-In, Refrigerator, Appliance Color: Stainless, Microwave: SS 1
Dishwasher, Double Sink, Garbage Disposal, Pantry: Walk-In, Refrigerator, Wet Bar, Appliance Color: Other 1
Dishwasher, Double Sink, Garbage Disposal, Refrigerator, Appliance Color: Stainless, Countertops: Granite 1
# of Ovens: 1, Dishwasher, Freezer, Garbage Disposal, Gas Range, Refrigerator, Appliance Color: Stainless, Countertops: Slate 1
Name: kitchen_features, Length: 1872, dtype: int64
df["floor_covering"].value_counts()
Carpet, Ceramic Tile 1235
Carpet, Natural Stone 579
Carpet, Ceramic Tile, Wood 258
Ceramic Tile 247
Concrete 242
...
Carpet, Other: brick 1
Carpet, Other: Travertine Tile 1
Carpet, Other: Travertine & slate 1
Natural Stone, Other: Travertine & Slate 1
Wood, Other: porcelain tile 1
Name: floor_covering, Length: 311, dtype: int64
def outliers(df):
for k, v in df.items():
q1 = v.quantile(0.25)
q3 = v.quantile(0.75)
iqr = q3 - q1
v_col = v[(v <= q1 - 1.5 * iqr) | (v >= q3 + 1.5 * iqr)]
percent = v_col.shape[0] * 100.0 / df.shape[0]
print("Column %s outliers = %.2f%%" % (k, percent))
outliers(df[["sold_price", "lot_acres", "taxes", "sqrt_ft", "HOA"]])
Column sold_price outliers = 7.90% Column lot_acres outliers = 10.46% Column taxes outliers = 5.54% Column sqrt_ft outliers = 4.76% Column HOA outliers = 1.90%
plt.figure(figsize=(20, 10))
sb.heatmap(df.corr(), annot=True);
fig, axs = plt.subplots(ncols=5, nrows=1, figsize=(20, 5))
index = 0
axs = axs.flatten()
for k,v in df[["sold_price", "lot_acres", "taxes", "sqrt_ft", "HOA"]].items():
sb.distplot(v, ax=axs[index])
index += 1
plt.tight_layout(pad=0.4, w_pad=0.5, h_pad=5.0)
df["sold_price"] = np.log1p(df["sold_price"])
df["lot_acres"] = np.log1p(df["lot_acres"])
df["taxes"] = np.log1p(df["taxes"])
df["sqrt_ft"] = np.log1p(df["sqrt_ft"])
df["HOA"] = np.log1p(df["HOA"])
sb.catplot(x="bedrooms", data=df);["sold_price", "lot_acres", "taxes", "sqrt_ft", "HOA"]
['sold_price', 'lot_acres', 'taxes', 'sqrt_ft', 'HOA']
sb.catplot(x="bathrooms", data=df);
df = df[df["bedrooms"] < 30]
df = df[df["bathrooms"] < 30]
fig, axs = plt.subplots(ncols=5, nrows=1, figsize=(20, 5))
index = 0
axs = axs.flatten()
for k,v in df[["sold_price", "lot_acres", "taxes", "sqrt_ft", "HOA"]].items():
sb.boxplot(x=k, data=df, ax=axs[index])
plt.xlabel(k)
index += 1
sb.pairplot(data = df[["sold_price", "lot_acres", "taxes", "sqrt_ft", "HOA"]]);
sb.pairplot(data = df[["sold_price", "lot_acres", "taxes", "sqrt_ft", "HOA"]], kind="kde");
profile = ProfileReport(df, title="Pandas Profiling Report")
profile
Summarize dataset: 100%|██████████| 30/30 [00:33<00:00, 1.11s/it, Completed] Generate report structure: 100%|██████████| 1/1 [00:06<00:00, 6.77s/it] Render HTML: 100%|██████████| 1/1 [00:05<00:00, 5.50s/it]
# May want to drop MLS, as it is some kind of ID
df[df["HOA"].isna() == True].count()
MLS 559 sold_price 559 zipcode 559 longitude 559 latitude 559 lot_acres 556 taxes 559 year_built 559 bedrooms 559 bathrooms 559 sqrt_ft 559 garage 559 kitchen_features 559 fireplaces 559 floor_covering 559 HOA 0 dtype: int64
df["HOA"] = df["HOA"].fillna(0)
df["fireplaces"] = df["fireplaces"].replace(' ', 0)
df["fireplaces"] = df["fireplaces"].astype("int")
sb.catplot(x="fireplaces", data=df);
df = df[df["fireplaces"] < 8]
outliers(df[["sold_price", "lot_acres", "taxes", "sqrt_ft", "HOA"]])
Column sold_price outliers = 4.47% Column lot_acres outliers = 5.41% Column taxes outliers = 6.91% Column sqrt_ft outliers = 2.54% Column HOA outliers = 0.00%
q1 = df["sqrt_ft"].quantile(0.25)
q3 = df["sqrt_ft"].quantile(0.75)
iqr = q3 - q1
df = df[df["sqrt_ft"] >= q1 - 1.5 * iqr]
df = df[df["sqrt_ft"] <= q3 + 1.5 * iqr]
outliers(df[["sold_price", "lot_acres", "taxes", "sqrt_ft", "HOA"]])
Column sold_price outliers = 4.03% Column lot_acres outliers = 5.36% Column taxes outliers = 6.41% Column sqrt_ft outliers = 0.43% Column HOA outliers = 0.00%
sb.boxplot(x="sqrt_ft", data=df);
len(df)/5000 * 100
97.32